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Analyzing large data sets requires proper understanding of the data in advance. This would help domain experts to influence the data mining process and to properly evaluate the results of a data mining application. This paper introduces an algorithm to identify anomalies in the data. The paper also proposes an approach to include the results of data characteristics checking in a data mining application. The application, reported in this paper, involves developing a disease model from gene expression data using machine learning techniques. The paper demonstrates how: simple models can be generated from a large set of attributes and the structure of the models change, when potentially anomalous cases are removed.
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